
import warnings

from ...utils import is_sklearn_available, requires_backends


if is_sklearn_available():
							from scipy.stats import pearsonr, spearmanr
							from sklearn.metrics import fa_score, matthews_corrcoef


lowerCamelCase_    :			List[str]    =	(
    """This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate """
    """library. You can have a look at this example script for pointers: """
    """https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"""
)







def 							A__       (    lowerCamelCase  ,     lowerCamelCase    )	->						Optional[Any]:
				warnings.warn(lowerCamelCase  ,     lowerCamelCase    )
				requires_backends(lowerCamelCase  ,     """sklearn"""    )
				return (preds == labels).mean()







def 							A__       (    lowerCamelCase  ,     lowerCamelCase    )	->						Any:
				warnings.warn(lowerCamelCase  ,     lowerCamelCase    )
				requires_backends(lowerCamelCase  ,     """sklearn"""    )
				UpperCamelCase_:				str						=    simple_accuracy(lowerCamelCase  ,     lowerCamelCase    )
				UpperCamelCase_:				Tuple						=    fa_score(y_true=lowerCamelCase  ,     y_pred=lowerCamelCase    )
				return {
				    "acc": acc,
				    "f1": fa,
				    "acc_and_f1": (acc + fa) / 2,
				}







def 							A__       (    lowerCamelCase  ,     lowerCamelCase    )	->						Optional[int]:
				warnings.warn(lowerCamelCase  ,     lowerCamelCase    )
				requires_backends(lowerCamelCase  ,     """sklearn"""    )
				UpperCamelCase_:				int						=    pearsonr(lowerCamelCase  ,     lowerCamelCase    )[0]
				UpperCamelCase_:				int						=    spearmanr(lowerCamelCase  ,     lowerCamelCase    )[0]
				return {
				    "pearson": pearson_corr,
				    "spearmanr": spearman_corr,
				    "corr": (pearson_corr + spearman_corr) / 2,
				}







def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )	->						List[Any]:
				warnings.warn(lowerCamelCase  ,     lowerCamelCase    )
				requires_backends(lowerCamelCase  ,     """sklearn"""    )
				assert len(lowerCamelCase    ) == len(lowerCamelCase    ), F'''Predictions and labels have mismatched lengths {len(lowerCamelCase    )} and {len(lowerCamelCase    )}'''
				if task_name == "cola":
								return {"mcc": matthews_corrcoef(lowerCamelCase  ,     lowerCamelCase    )}
				elif task_name == "sst-2":
								return {"acc": simple_accuracy(lowerCamelCase  ,     lowerCamelCase    )}
				elif task_name == "mrpc":
								return acc_and_fa(lowerCamelCase  ,     lowerCamelCase    )
				elif task_name == "sts-b":
								return pearson_and_spearman(lowerCamelCase  ,     lowerCamelCase    )
				elif task_name == "qqp":
								return acc_and_fa(lowerCamelCase  ,     lowerCamelCase    )
				elif task_name == "mnli":
								return {"mnli/acc": simple_accuracy(lowerCamelCase  ,     lowerCamelCase    )}
				elif task_name == "mnli-mm":
								return {"mnli-mm/acc": simple_accuracy(lowerCamelCase  ,     lowerCamelCase    )}
				elif task_name == "qnli":
								return {"acc": simple_accuracy(lowerCamelCase  ,     lowerCamelCase    )}
				elif task_name == "rte":
								return {"acc": simple_accuracy(lowerCamelCase  ,     lowerCamelCase    )}
				elif task_name == "wnli":
								return {"acc": simple_accuracy(lowerCamelCase  ,     lowerCamelCase    )}
				elif task_name == "hans":
								return {"acc": simple_accuracy(lowerCamelCase  ,     lowerCamelCase    )}
				else:
								raise KeyError(lowerCamelCase    )







def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )	->						str:
				warnings.warn(lowerCamelCase  ,     lowerCamelCase    )
				requires_backends(lowerCamelCase  ,     """sklearn"""    )
				if len(lowerCamelCase    ) != len(lowerCamelCase    ):
								raise ValueError(F'''Predictions and labels have mismatched lengths {len(lowerCamelCase    )} and {len(lowerCamelCase    )}'''    )
				if task_name == "xnli":
								return {"acc": simple_accuracy(lowerCamelCase  ,     lowerCamelCase    )}
				else:
								raise KeyError(lowerCamelCase    )








import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
							lowerCamelCase_    :			int    =	argparse.ArgumentParser()

							parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")

							parser.add_argument(
							    """--txt2img_unclip""",
							    default="""kakaobrain/karlo-v1-alpha""",
							    type=str,
							    required=False,
							    help="""The pretrained txt2img unclip.""",
							)

							lowerCamelCase_    :			Dict    =	parser.parse_args()

							lowerCamelCase_    :			str    =	UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

							lowerCamelCase_    :			int    =	CLIPImageProcessor()
							lowerCamelCase_    :			Any    =	CLIPVisionModelWithProjection.from_pretrained("""openai/clip-vit-large-patch14""")

							lowerCamelCase_    :			Union[str, Any]    =	UnCLIPImageVariationPipeline(
							    decoder=txtaimg.decoder,
							    text_encoder=txtaimg.text_encoder,
							    tokenizer=txtaimg.tokenizer,
							    text_proj=txtaimg.text_proj,
							    feature_extractor=feature_extractor,
							    image_encoder=image_encoder,
							    super_res_first=txtaimg.super_res_first,
							    super_res_last=txtaimg.super_res_last,
							    decoder_scheduler=txtaimg.decoder_scheduler,
							    super_res_scheduler=txtaimg.super_res_scheduler,
							)

							imgaimg.save_pretrained(args.dump_path)






